

from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType



@dataclass
class        A__:
						"""simple docstring"""






						_A    :				List[str]
						_A    :				Optional[str]       					=			None
						# Automatically constructed
						_A    :				ClassVar[str]       					=			"dict"
						_A    :				ClassVar[Any]       					=			None
						_A    :				str       					=			field(default='''Translation''',  init=a_,  repr=a_							)





						def __call__(    self    )     -> Optional[Any]:
									return pa.struct({lang: pa.string() for lang in sorted(self.languages    )}    )





						def 			UpperCamelCase__					(    self    )     -> Union["FeatureType", Dict[str, "FeatureType"]]:
									from .features import Value

									return {k: Value("""string"""    ) for k in sorted(self.languages    )}







@dataclass
class        A__:
						"""simple docstring"""






						_A    :				Optional[List]       					=			None
						_A    :				Optional[int]       					=			None
						_A    :				Optional[str]       					=			None
						# Automatically constructed
						_A    :				ClassVar[str]       					=			"dict"
						_A    :				ClassVar[Any]       					=			None
						_A    :				str       					=			field(default='''TranslationVariableLanguages''',  init=a_,  repr=a_							)





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[str] 							= sorted(set(self.languages    )    ) if self.languages else None
									a_						:						Optional[Any] 							= len(self.languages    ) if self.languages else None





						def __call__(    self    )     -> int:
									return pa.struct({"""language""": pa.list_(pa.string()    ), """translation""": pa.list_(pa.string()    )}    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						Optional[int] 							= set(self.languages    )
									if self.languages and set(_lowercase    ) - lang_set:
												raise ValueError(
												    F'''Some languages in example ({", ".join(sorted(set(_lowercase    ) - lang_set    )    )}) are not in valid set ({", ".join(_lowercase    )}).'''    )

									# Convert dictionary into tuples, splitting out cases where there are
									# multiple translations for a single language.
									a_						:						Optional[int] 							= []
									for lang, text in translation_dict.items():
												if isinstance(_lowercase  ,      _lowercase    ):
															translation_tuples.append((lang, text)    )
												else:
															translation_tuples.extend([(lang, el) for el in text]    )

        # Ensure translations are in ascending order by language code.
									a_							,					a_						:						Dict 							= zip(*sorted(_lowercase    )    )

									return {"language": languages, "translation": translations}





						def 			UpperCamelCase__					(    self    )     -> Union["FeatureType", Dict[str, "FeatureType"]]:
									from .features import Sequence, Value

									return {
									    "language": Sequence(Value("""string"""    )    ),
									    "translation": Sequence(Value("""string"""    )    ),
									}





from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= SMALL_MODEL_IDENTIFIER
									a_						:						Optional[int] 							= """pt"""
									a_						:						Union[str, Any] 							= """tf"""





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						Union[str, Any] 							= AutoModel.from_pretrained(self.test_model    )
									model_pt.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[str]:
									a_						:						int 							= TFAutoModel.from_pretrained(self.test_model  ,      from_pt=_lowercase    )
									model_tf.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						str 							= """mock_framework"""

									# Framework provided - return whatever the user provides
									a_						:						Optional[int] 							= FeaturesManager.determine_framework(self.test_model  ,      _lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )

									# Local checkpoint and framework provided - return provided framework
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Optional[int] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						List[str] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Invalid local checkpoint
									with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(_lowercase    ):
															a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						int 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# PyTorch not in environment -> use TensorFlow
									a_						:						Dict 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Both in environment -> use PyTorch
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									a_						:						List[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Any 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# Both not in environment -> raise error
									a_						:						List[str] 							= MagicMock(return_value=_lowercase    )
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												with self.assertRaises(_lowercase    ):
															a_						:						Dict 							= FeaturesManager.determine_framework(self.test_model    )


